scholarly journals Force of infection ofHelicobacter pyloriin Mexico: evidence from a national survey using a hierarchical Bayesian model

2018 ◽  
Vol 146 (8) ◽  
pp. 961-969 ◽  
Author(s):  
F. Alarid-Escudero ◽  
E. A. Enns ◽  
R. F. MacLehose ◽  
J. Parsonnet ◽  
J. Torres ◽  
...  
Keyword(s):  
Cumulative Risk ◽  
Random Variable ◽  
Credible Interval ◽  
Specific Force ◽  
Risk Of Infection ◽  
Susceptible Population ◽  
Bayesian Hierarchical ◽  
Force Of Infection ◽  
Binomial Random Variable ◽  
H Pylori

AbstractHelicobacter pylori(H. pylori)is present in the stomach of half of the world's population. The force of infection describes the rate at which susceptibles acquire infection. In this article, we estimated the age-specific force of infection ofH. pyloriin Mexico. Data came from a nationalH. pyloriseroepidemiology survey collected in Mexico in 1987–88. We modelled the number of individuals withH. pyloriat a given age as a binomial random variable. We assumed that the cumulative risk of infection by a given age follows a modified exponential catalytic model, allowing some fraction of the population to remain uninfected. The cumulative risk of infection was modelled for each state in Mexico and were shrunk towards the overall national cumulative risk curve using Bayesian hierarchical models. The proportion of the population that can be infected (i.e. susceptible population) is 85.9% (95% credible interval (CR) 84.3%–87.5%). The constant rate of infection per year of age among the susceptible population is 0.092 (95% CR 0.084–0.100). The estimated force of infection was highest at birth 0.079 (95% CR 0.071–0.087) decreasing to zero as age increases. This Bayesian hierarchical model allows stable estimation of state-specific force of infection by pooling information between the states, resulting in more realistic estimates.

scholarly journals A hierarchical model to estimate the abundance and biomass of salmonids by using removal sampling and biometric data from multiple locations

2010 ◽  
Vol 67 (12) ◽  
pp. 2032-2044 ◽  
Author(s):  
Philippe Ruiz ◽  
Christophe Laplanche
Keyword(s):  
Spatial Variability ◽  
Hierarchical Model ◽  
Salmo Trutta ◽  
Credible Interval ◽  
Fish Length ◽  
Biometric Data ◽  
Bayesian Hierarchical ◽  
Fish Model ◽  
Brown Trout Salmo Trutta ◽  
Removal Sampling

We present a Bayesian hierarchical model to estimate the abundance and the biomass of brown trout ( Salmo trutta fario ) by using removal sampling and biometric data collected at several stream sections. The model accounts for (i) variability of the abundance with fish length (as a distribution mixture), (ii) spatial variability of the abundance, (iii) variability of the catchability with fish length (as a logit regression model), (iv) spatial variability of the catchability, and (v) residual variability of the catchability with fish. Model measured variables are the areas of the stream sections as well as the length and the weight of the caught fish. We first test the model by using a simulated dataset before using a 3-location, 2-removal sampling dataset collected in the field. Fifteen model alternatives are compared with an index of complexity and fit by using the field dataset. The selected model accounts for variability of the abundance with fish length and stream section and variability of the catchability with fish length. By using the selected model, 95% credible interval estimates of the abundances at the three stream sections are (0.46,0.59), (0.90,1.07), and (0.56,0.69) fish/m2. Respective biomass estimates are (9.68, 13.58), (17.22, 22.71), and (12.69, 17.31) g/m2.

scholarly journals Year-round monitoring reveals prevalence of fatal bird-window collisions at the Virginia Tech Corporate Research Center

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4562 ◽  
Author(s):  
Rebecca M. Schneider ◽  
Christine M. Barton ◽  
Keith W. Zirkle ◽  
Caitlin F. Greene ◽  
Kara B. Newman
Keyword(s):  
Relative Risk ◽  
Land Cover ◽  
Poisson Model ◽  
Credible Interval ◽  
The United States ◽  
Virginia Tech ◽  
Research Center ◽  
Bayesian Hierarchical ◽  
Significant Relationships ◽  
Ornamental Trees

Collisions with glass are a serious threat to avian life and are estimated to kill hundreds of millions of birds per year in the United States. We monitored 22 buildings at the Virginia Tech Corporate Research Center (VTCRC) in Blacksburg, Virginia, for collision fatalities from October 2013 through May 2015 and explored possible effects exerted by glass area and surrounding land cover on avian mortality. We documented 240 individuals representing 55 identifiable species that died due to collisions with windows at the VTCRC. The relative risk of fatal collisions at all buildings over the study period were estimated using a Bayesian hierarchical zero-inflated Poisson model adjusting for percentage of tree and lawn cover within 50 m of buildings, as well as for glass area. We found significant relationships between fatalities and surrounding lawn area (relative risk: 0.96, 95% credible interval: 0.93, 0.98) as well as glass area on buildings (RR: 1.30, 95% CI [1.05–1.65]). The model also found a moderately significant relationship between fatal collisions and the percent land cover of ornamental trees surrounding buildings (RR = 1.02, 95% CI [1.00–1.05]). Every building surveyed had at least one recorded collision death. Our findings indicate that birds collide with VTCRC windows during the summer breeding season in addition to spring and fall migration. The Ruby-throated Hummingbird (Archilochus colubris) was the most common window collision species and accounted for 10% of deaths. Though research has identified various correlates with fatal bird-window collisions, such studies rarely culminate in mitigation. We hope our study brings attention, and ultimately action, to address this significant threat to birds at the VTCRC and elsewhere.

The Stochastic Delta Rule: Faster and More Accurate Deep Learning Through Adaptive Weight Noise

2020 ◽  
Vol 32 (5) ◽  
pp. 1018-1032 ◽  
Author(s):  
Noah Frazier-Logue ◽  
Stephen José Hanson
Keyword(s):  
Prediction Error ◽  
Saddle Points ◽  
Model Averaging ◽  
Random Variable ◽  
Mean Values ◽  
Delta Rule ◽  
Binomial Random Variable ◽  
Optimal Network ◽  
Training Error ◽  
Feature Discovery

Multilayer neural networks have led to remarkable performance on many kinds of benchmark tasks in text, speech, and image processing. Nonlinear parameter estimation in hierarchical models is known to be subject to overfitting and misspecification. One approach to these estimation and related problems (e.g., saddle points, colinearity, feature discovery) is called Dropout. The Dropout algorithm removes hidden units according to a binomial random variable with probability [Formula: see text] prior to each update, creating random “shocks” to the network that are averaged over updates (thus creating weight sharing). In this letter, we reestablish an older parameter search method and show that Dropout is a special case of this more general model, stochastic delta rule (SDR), published originally in 1990. Unlike Dropout, SDR redefines each weight in the network as a random variable with mean [Formula: see text] and standard deviation [Formula: see text]. Each weight random variable is sampled on each forward activation, consequently creating an exponential number of potential networks with shared weights (accumulated in the mean values). Both parameters are updated according to prediction error, thus resulting in weight noise injections that reflect a local history of prediction error and local model averaging. SDR therefore implements a more sensitive local gradient-dependent simulated annealing per weight converging in the limit to a Bayes optimal network. We run tests on standard benchmarks (CIFAR and ImageNet) using a modified version of DenseNet and show that SDR outperforms standard Dropout in top-5 validation error by approximately 13% with DenseNet-BC 121 on ImageNet and find various validation error improvements in smaller networks. We also show that SDR reaches the same accuracy that Dropout attains in 100 epochs in as few as 40 epochs, as well as improvements in training error by as much as 80%.

scholarly journals Estimating the Force of Infection with Helicobacter pylori in Japan

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Taishi Kayano ◽  
Ki-Deok Lee ◽  
Hiroshi Nishiura
Keyword(s):  
Gastric Cancer ◽  
Helicobacter Pylori ◽  
World War Ii ◽  
Time Course ◽  
Force Of Infection ◽  
Seroprevalence Data ◽  
Age Dependent ◽  
The Future ◽  
Parameterized Model ◽  
H Pylori

Background. Although the seroprevalence against Helicobacter pylori (H. pylori) in Japan has declined over the birth year, Japanese people have yet exhibited a relatively high risk of gastric cancer. The present study employed mathematical models to estimate the time- and age-dependent force of infection with H. pylori in Japan, predicting the future seroprevalence by time and age. Methods. We investigated the published seroprevalence data against H. pylori in Japan from 1980–2018. Solving the McKendrick partial differential equation model, the seroprevalence was modeled as a function of survey year and age. Maximum likelihood estimation was conducted to estimate parameters governing the time- and age-dependent force of infection. Results. Among all fitted models, the time-dependent and age-independent model with an exponentially decaying force of infection over years was most favored. Fitted models indicated that the force of infection started to decrease during and/or shortly after the World War II. Using the parameterized model, the predicted fraction seropositive at the age of 40 years in 2018 was 0.22, but it is expected to decrease to 0.13 in 2030 and 0.05 in 2050, respectively. Conclusion. The time dependence was consistent with the decline in the force of infection as a function of the birth year. The force of infection has continuously and greatly declined over time, implying the diminished transmission of H. pylori through the time course and small chance of persistence. These findings are critical to anticipate the future decline in gastric cancer incidence.

scholarly journals A refinement of normal approximation to Poisson binomial

2005 ◽  
Vol 2005 (5) ◽  
pp. 717-728 ◽  
Author(s):  
K. Neammanee
Keyword(s):  
Normal Distribution ◽  
Normal Approximation ◽  
Random Variables ◽  
Random Variable ◽  
Standard Normal Distribution ◽  
Bernoulli Random Variables ◽  
Binomial Random Variable ◽  
Standard Normal ◽  
Correction Terms ◽  
Better Than

LetX1,X2,…,Xnbe independent Bernoulli random variables withP(Xj=1)=1−P(Xj=0)=pjand letSn:=X1+X2+⋯+Xn.Snis called a Poisson binomial random variable and it is well known that the distribution of a Poisson binomial random variable can be approximated by the standard normal distribution. In this paper, we use Taylor's formula to improve the approximation by adding some correction terms. Our result is better than before and is of order1/nin the casep1=p2=⋯=pn.

Understanding the long-term concentration, flux, composition and processing of dissolved organic carbon in UK rivers

2020 ◽  
Author(s):  
Fred Worrall ◽  
Nicholas Howden ◽  
Timothy Burt
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Dissolved Organic Matter ◽  
Dissolved Organic Carbon ◽  
Sewage Treatment ◽  
Resource Limitation ◽  
Credible Interval ◽  
Bayesian Hierarchical ◽  
Doc Flux ◽  
The Uk

<p>Dissolved organic carbon (DOC) represents an important component of the terrestrial and fluvial carbon cycle as it represents a flux from terrestrial carbon stores and while it transfers through the fluvial network it can be processed to release greenhouse gases to the atmosphere. Furthermore, DOC is a major water resource limitation as the dissolved organic matter has to be removed prior to treatment. Therefore, we need to understand the concentration and fluxes of DOC and they change across a landscape between the terrestrial source and the tidal limit.</p><p>Our ability to understand the processing of terrestrial and fluvial carbon has been limited by the range of catchments that have been considered and the time scale over which they have been considered. Studies focused on similar catchment types and very little means of comparing between catchments. However, if we can access and understand large datasets we can find general principles which control DOC and the relative importance of these controls. In this study we use two datasets. The first, is a dataset sampled across the UK for major rivers (270 catchments) from 1974 and this dataset is ideal for understanding flux to the continental shelf and this dataset has over 50000 datapoints. Secondly, many of these sites are monitored for a rang e of other parameters that are related to the composition of the dissolved organic matter. The important covariates for DOM composition are BOD, which is a measure of DOM decomposition, and COD which is measure of the oxidation state of the DOM. All the study catchments could be characterised by a range of covariate information, eg. soil cover, land use, hydro-climatology. To make maximum use of this data the dataset was considered within a Bayesian hierarchical framework.</p><p>The concentrations of DOC from the UK rose for the 1974 on to the late 1990s before a decline to 2007-08. The decline was driven by changes in urban sources, particular by improvements in sewage treatment. The DOC flux from the UK has declined since a peak in 2000 and in 2017 was 767 ktonnes C/yr (95% credible interval 644 – 909 ktonnesC/yr). Modelling composition turnover gives the DOC flux from source as 3.5 Mtonnes C/yr with 2.6 Mtonnes C/yr lost to atmosphere (14 Mtonnes CO<sub>2eq</sub>/yr = 59 tonnes CO<sub>2eq</sub>/km2/yr).</p>

scholarly journals Analyzing Postdisaster Surveillance Data: The Effect of the Statistical Method

2008 ◽  
Vol 2 (2) ◽  
pp. 119-126 ◽  
Author(s):  
Charles DiMaggio ◽  
Sandro Galea ◽  
David Abramson
Keyword(s):  
Survey Data ◽  
Hierarchical Modeling ◽  
Terrorist Attack ◽  
Credible Interval ◽  
Administrative Databases ◽  
Point Estimate ◽  
Bayesian Hierarchical Modeling ◽  
Measures Of Association ◽  
Bayesian Approaches ◽  
Bayesian Hierarchical

ABSTRACTData from existing administrative databases and ongoing surveys or surveillance methods may prove indispensable after mass traumas as a way of providing information that may be useful to emergency planners and practitioners. The analytic approach, however, may affect exposure prevalence estimates and measures of association. We compare Bayesian hierarchical modeling methods to standard survey analytic techniques for survey data collected in the aftermath of a terrorist attack. Estimates for the prevalence of exposure to the terrorist attacks of September 11, 2001, varied by the method chosen. Bayesian hierarchical modeling returned the lowest estimate for exposure prevalence with a credible interval spanning nearly 3 times the range of the confidence intervals (CIs) associated with both unadjusted and survey procedures. Bayesian hierarchical modeling also returned a smaller point estimate for measures of association, although in this instance the credible interval was tighter than that obtained through survey procedures. Bayesian approaches allow a consideration of preexisting assumptions about survey data, and may offer potential advantages, particularly in the uncertain environment of postterrorism and disaster settings. Additional comparative analyses of existing data are necessary to guide our ability to use these techniques in future incidents. (Disaster Med Public Health Preparedness. 2008;2:119–126)

scholarly journals Identifying age cohorts responsible for Peste des petits ruminants virus transmission among sheep, goats, and cattle in northern Tanzania

2019 ◽  
Author(s):  
C. M. Herzog ◽  
W. A. de Glanville ◽  
B. J. Willett ◽  
I. M. Cattadori ◽  
V. Kapur ◽  
...  
Keyword(s):  
Age Groups ◽  
Virus Transmission ◽  
Sufficient Evidence ◽  
Specific Force ◽  
Age Group ◽  
Peste Des Petits Ruminants ◽  
Transmission Rates ◽  
Force Of Infection ◽  
Pastoral Cattle ◽  
Best Fitting

AbstractPeste des petits ruminants virus (PPRV) causes a contagious disease of high morbidity and mortality in global sheep and goat populations and leads to approximately $2 billion USD in global annual losses. PPRV is currently targeted by the Food and Agricultural Organization and World Animal Health Organization for global eradication by 2030. To better control this disease and inform eradication strategies, an improved understanding of how PPRV risk varies by age is needed. Our study used a piece-wise catalytic model to estimate the age-specific force of infection (FOI, per capita infection rate of susceptible hosts) among sheep, goats, and cattle from a cross-sectional serosurvey dataset collected in 2016 in Tanzania. Apparent seroprevalence rose with age, as would be expected if PPRV is a fully-immunizing infection, reaching 53.6%, 46.8%, and 11.6% (true seroprevalence: 52.7%, 52.8%, 39.2%) for sheep, goats, and cattle, respectively. Seroprevalence was significantly higher among pastoral animals than agropastoral animals across all ages, with pastoral sheep and goat seroprevalence approaching 70% and 80%, respectively, suggesting endemicity in pastoral settings. The best fitting piece-wise catalytic models included merged age groups: two age groups for sheep, three age groups for goats, and four age groups for cattle. However, the signal of these age heterogeneities was weak, with overlapping confidence intervals around force of infection estimates from most models with the exception of a significant FOI peak among 2.5-3.5 year old pastoral cattle. Pastoral animals had a higher force of infection overall, and across a wider range of ages than agropastoral animals. The subtle age-specific force of infection heterogeneities identified in this study among sheep, goats, and cattle suggest that targeting control efforts by age may not be as effective as targeting by other risk factors, such as management system type. Further research should investigate how specific husbandry practices affect PPRV transmission.Author SummaryAge differences in transmission are important for many infections, and can help target control programs. We used an age-structured serosurvey of Tanzanian sheep, goats, and cattle to explore peste des petits ruminants virus transmission. We estimated rate at which susceptibles acquire infection (force of infection) to determine which age group(s) had the highest transmission rates. We hypothesized that an age-varying model with multiple age groups would better fit the data than an age constant model and that the highest transmission rates would appear in the youngest age groups. Furthermore, we hypothesized evidence of immunity would increase with age. The data supported our hypothesis at the species level and the best fitting models merged age groups: two, three, and four age group models were best for sheep, goats, and cattle, respectively. The highest rates occurred among younger age groups and evidence of immunity rose with age for all species. In most models, confidence interval estimates overlapped, but there was a significant FOI peak among 2.3-3.5 year old pastoral cattle. Importantly, these data indicate that there is not sufficient evidence to support targeted control by age group, and that targeted control based on production system should be more effective.

scholarly journals Modelling the population dynamics of Plasmodium falciparum gametocytes in humans during malaria infection

2019 ◽  
Author(s):  
Pengxing Cao ◽  
Katharine A. Collins ◽  
Sophie Zaloumis ◽  
Thanaporn Wattanakul ◽  
Joel Tarning ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Plasmodium Falciparum ◽  
Population Dynamics ◽  
Malaria Infection ◽  
Credible Interval ◽  
The Novel ◽  
Reliable Prediction ◽  
Bayesian Hierarchical ◽  
Kinetics Of

AbstractEvery year over two hundred million people are infected with the malaria parasite. Renewed efforts to eliminate malaria has highlighted the potential to interrupt transmission from humans to mosquitoes which is mediated through the gametocytes. Reliable prediction of transmission requires an improved understanding of in vivo kinetics of gametocytes. Here we study the population dynamics of Plasmodium falciparum gametocytes in human hosts by establishing a framework which incorporates improved measurements of parasitaemia in humans, a novel mathematical model of gametocyte dynamics, and model validation using a Bayesian hierarchical inference method. We found that the novel mathematical model provides an excellent fit to the available clinical data from 17 volunteers infected with P. falciparum, and reliably predicts observed gametocyte levels. We estimated the P. falciparum’s sexual commitment rate and gametocyte sequestration time in humans to be 0.54% (95% credible interval: 0.30-1.00) per life cycle and 8.39 (6.54-10.59) days respectively. Furthermore, we used the data-calibrated model to predict the effects of those gametocyte dynamics parameters on human-to-mosquito transmissibility, providing a method to link within-human host kinetics of malaria infection to epidemiological-scale infection and transmission patterns.

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